Multi-element electronic warranty mobile service method

Abstract

The invention discloses a multi-element electronic warranty mobile service method. According to the method, the logistic regression algorithm can effectively identify the potential high-risk warranty application by analyzing detailed information and historical data provided by the user. The process comprises data preparation, feature selection, model training, verification and application, and the accuracy and reliability of risk assessment are ensured. Through the step, the system can screen out applications which may have default risks in advance, so that an insurance company or a guarantee company is protected from unnecessary economic loss. According to the automatic risk assessment mechanism, the auditing efficiency is greatly improved, the workload of manual auditing is reduced, and meanwhile, the misjudgment risk caused by human errors is also reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of electronic guarantee services, and specifically relates to a method for mobile services of diversified electronic guarantees. Background Art

[0002] Mobile services of electronic guarantees refer to providing services such as online handling, querying, and management of guarantee business for users through mobile Internet technology. Mobile services of electronic guarantees use mobile devices such as smart phones and tablet computers as carriers, and rely on the advantages of the Internet to achieve convenient handling of guarantee business. Since the launch of this service, it has been welcomed by enterprises and individuals, providing efficient and secure guarantee services for fields such as finance, construction, and bidding. With the popularization of mobile Internet, the application of mobile services of electronic guarantees in industries such as finance and construction will be more extensive. In the future, mobile services of electronic guarantees will continuously improve their functions, enhance user experience, and meet the needs of more scenarios. Mobile services of electronic guarantees, with their advantages of convenience, security, and efficiency, have become a major innovation in the field of fintech. In future development, this service will continue to promote the transformation and upgrading of related industries and contribute to the economic development of our country. In short, mobile services of electronic guarantees provide a convenient guarantee solution for enterprises and individuals, and are expected to achieve more innovative applications in the future.

[0003] However, traditional risk assessment may rely on manual judgment, which is subjective and inconsistent, and prone to misjudgment. At the same time, there is no effective mechanism to pre-screen high-risk applications in advance, which may cause economic losses to insurance companies or guarantee companies. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for mobile services of diversified electronic guarantees to solve the above-mentioned problems.

[0005] The technical solution adopted by the present invention is as follows: A method for mobile services of diversified electronic guarantees, which includes the following steps:

[0006] S1: User registration and authentication. After downloading and installing the diversified electronic guarantee APP on the mobile terminal, the user fills in personal information, including name, ID number, and contact information; and uploads ID cards, business license and other certification materials for real-name authentication.

[0007] S2: Select guarantee products. The user browses various electronic guarantee products in the APP and selects appropriate guarantee products according to needs, including bid bonds and performance bonds.

[0008] S3: Fill in guarantee application information. The user fills in application information according to the selected guarantee product, including project name, amount, and term; and then uploads relevant contracts, bidding documents and other certification materials.

[0009] S4: Conduct risk assessment. The system conducts risk assessment based on the information filled in by the user and the materials uploaded. For high-risk projects, the system prompts the user for manual review.

[0010] S5: Confirm the guarantee fee. The user views the details of the guarantee fee. After confirming that it is correct, the user selects the payment method.

[0011] S6: Conduct online payment. The user completes the payment of the guarantee fee through the payment function within the APP. After successful payment, the system generates a payment voucher.

[0012] S7: Conduct guarantee review and issuance. The system submits the application information and the payment voucher. Then it conducts a review, and issues an electronic guarantee after the review is passed.

[0013] S8: Push and query the guarantee. The system pushes the electronic guarantee to the user. The user can view, download and print the electronic guarantee within the APP and can query the guarantee status and relevant information at any time.

[0014] S9: Extend and revoke the guarantee. Before the guarantee expires, the user can apply for extension within the APP. If the user needs to revoke the guarantee, the user can submit a revocation application within the APP. After the review is passed, the extension or revocation procedures are handled.

[0015] S10: Provide after-sales service and handle complaints. If the user has any questions or problems during the use process, the user can consult through the customer service function within the APP. The customer service staff responds and solves the problems in a timely manner. The user can evaluate the service, put forward suggestions or complaints.

[0016] In a preferred embodiment, in step S1, in order to improve the authentication efficiency and security, the system adopts an advanced face recognition algorithm to ensure the authenticity of the user's identity through face comparison. During the authentication process, the system also requires the user to perform a live detection to prevent fraud. Once the authentication is successful, the system creates a unique personal account for the user, and the user can perform all subsequent operations through this account.

[0017] In a preferred embodiment, in step S2, the system provides a detailed product introduction, including the applicable scope, advantages, and fee standards of each guarantee. The user selects the most suitable guarantee product according to their own business needs. In addition, the system uses a content-based recommendation algorithm, which analyzes the user's personal information, historical browsing records, and industry preferences to recommend high-matching guarantee products to the user, thereby enhancing the user experience.

[0018] In a preferred embodiment, in step S3, to improve the accuracy and efficiency of information filling, the system adopts natural language processing technology, especially the named entity recognition algorithm, which can automatically identify and extract key information in the user input text, reduce manual input errors, and speed up the application process.

[0019] In a preferred embodiment, in step S4, the logistic regression algorithm is used to classify "high risk" and "low risk" in risk assessment, specifically including:

[0020] S4-1. Data preparation: Collect and preprocess data, including user characteristics and project characteristics; S4-2. Feature selection: Determine which features are most important for risk assessment;

[0021] S4-3. Model training: Use historical data to train the logistic regression model;

[0022] S4-4. Model validation: Verify the effectiveness of the model through the cross-validation method;

[0023] S4-5. Model application: Use the trained model to conduct risk assessment on new guarantee applications;

[0024] The basic formula of logistic regression is:

[0025]

[0026] The calculation formula of probability:

[0027]

[0028] Where:

[0029] P(Y = 1|X) is the probability of event Y = 1 (i.e., high risk) given feature X;

[0030] β 0 is the intercept term, indicating the log-odds of risk when all features XX are 0;

[0031] β1, β2,..., β n are the coefficients of each feature X 1 , X 2 ,..., X n indicating the degree of influence of each feature on risk; the positive or negative of the coefficient indicates the positive or negative correlation between the feature and risk;

[0032] X 1 , X 2 ,..., X n are feature variables, including personal credit score, project size, and contract amount.

[0033] In a preferred embodiment, in step S5, during the cost calculation process, the system uses an intelligent pricing algorithm based on cost. This algorithm comprehensively considers project risks, guarantee amounts, and market conditions to formulate a reasonable cost standard for users.

[0034] In a preferred embodiment, in step S6, to ensure payment security, the system uses the RSA encryption algorithm to ensure the security of users' payment information during transmission. After successful payment, the system immediately generates a payment voucher and provides it for users to view and download for subsequent verification and auditing.

[0035] In step S7, a decision tree algorithm is used to review the application materials. This algorithm can quickly identify applications that meet the conditions or have problems. After passing the review, an electronic guarantee will be issued within a short time and feedback to the user through the system.

[0036] In a preferred embodiment, in step S8, to ensure that users can obtain guarantee information in real time, the system uses the WebSocket protocol for data synchronization to keep the user interface and server data updated in real time.

[0037] In a preferred embodiment, in step S9, a support vector machine algorithm is used to review extension or cancellation applications. After passing the review, the system will handle the extension or cancellation procedures for users and update the guarantee status.

[0038] In a preferred embodiment, in step S9, to improve the quality of after-sales service, the system uses a sentiment analysis algorithm to analyze the sentiment tendency of user feedback, so as to better understand user needs, optimize service strategies, and improve user satisfaction.

[0039] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present invention are:

[0040] 1. In the present invention, the logistic regression algorithm can effectively identify potential high-risk guarantee applications by analyzing the detailed information and historical data provided by users. This process includes data preparation, feature selection, model training, verification, and application, ensuring the accuracy and reliability of risk assessment. Through this step, the system can pre-screen applications that may have default risks, thus protecting insurance companies or guarantee companies from unnecessary economic losses. This automated risk assessment mechanism greatly improves the review efficiency, reduces the workload of manual review, and also reduces the risk of misjudgment caused by human errors.

[0041] 2. In the present invention, the classification results of the logistic regression algorithm provide clear decision-making support for users and insurance companies. For applications classified as high-risk, the system will prompt for manual review, which is not only a supplement to the risk control of insurance companies but also a test of users' integrity. This dual review mechanism ensures that only eligible applicants can obtain the guarantee letter, thus maintaining the fairness and order of the market. In addition, by quantifying risks through the logistic regression algorithm, insurance companies can more precisely formulate the fees for guarantee letters and provide more reasonable quotes for users, which not only improves the user experience but also enhances the market competitiveness of insurance companies. Generally speaking, the application of the logistic regression algorithm has significantly improved the multi-element electronic guarantee letter mobile service method in terms of risk management and user service, providing strong support for the smooth operation of the entire service process. Detailed implementation manners

[0042] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0043] Embodiment:

[0044] A multi-element electronic guarantee letter mobile service method includes the following steps:

[0045] S1: Perform user registration and authentication. After downloading and installing the multi-element electronic guarantee letter APP on the mobile terminal, the user fills in personal information, including name, ID number, and contact information; and uploads proof materials such as ID cards and business licenses for real-name authentication.

[0046] S2: Select a guarantee letter product. The user browses various types of electronic guarantee letter products in the APP and selects a suitable guarantee letter product according to the needs, such as a tender guarantee letter or a performance guarantee letter.

[0047] S3: Fill in the guarantee letter application information. The user fills in the application information according to the selected guarantee letter product, such as project name, amount, and term. Then upload relevant contract and tender document proof materials.

[0048] S4: Conduct risk assessment. The system conducts a risk assessment based on the information filled in by the user and the uploaded materials. For high-risk projects, the system prompts the user for manual review.

[0049] S5: Confirm the guarantee letter fee. The user views the guarantee letter fee details. After confirmation, select the payment method.

[0050] S6: Conduct online payment. The user completes the payment of the guarantee letter fee through the payment function in the APP. After successful payment, the system generates a payment voucher.

[0051] S7: Conduct letter of guarantee review and issuance. The system submits the application information and payment vouchers to the insurance company or guarantee company. Then, the insurance company or guarantee company conducts a review. After passing the review, an electronic letter of guarantee is issued;

[0052] S8: Conduct letter of guarantee push and query. The system pushes the electronic letter of guarantee to the user. The user can view, download, and print the electronic letter of guarantee within the APP and can query the status and relevant information of the letter of guarantee at any time;

[0053] S9: Conduct letter of guarantee extension and revocation. Before the expiration of the letter of guarantee, the user can apply for extension within the APP. If the user needs to revoke the letter of guarantee, the user can submit a revocation application within the APP. After the insurance company or guarantee company approves the review, the extension or revocation procedures are handled;

[0054] S10: Conduct after-sales service and complaint handling. If the user has any questions or problems during the use process, the user can consult through the customer service function within the APP. The customer service staff responds and solves the problems in a timely manner.

[0055] The user can evaluate the service, make suggestions or file complaints.

[0056] In step S1, to improve the authentication efficiency and security, the system adopts an advanced face recognition algorithm, such as the ArcFace algorithm, to ensure the authenticity of the user's identity through face comparison. During the authentication process, the system will also require the user to perform a live detection to prevent fraud. Once the authentication is successful, the system will create a unique personal account for the user, and the user can perform all subsequent operations through this account.

[0057] In step S2, the system provides a detailed product introduction, including the scope of application, advantages, and fee standards of each letter of guarantee. The user can select the most suitable letter of guarantee product according to their business needs. In addition, the system uses a content-based recommendation algorithm, which analyzes the user's personal information, historical browsing records, and industry preferences to recommend high-matching letter of guarantee products for the user, thereby enhancing the user experience.

[0058] In step S3, to improve the accuracy and efficiency of information filling, the system adopts natural language processing technology, especially the named entity recognition algorithm, which can automatically identify and extract key information in the text input by the user, reduce manual input errors, and speed up the application process.

[0059] In step S4, the logistic regression algorithm is used to classify "high risk" and "low risk" in risk assessment, specifically including:

[0060] S4-1. Data preparation: Collect and preprocess data, including user characteristics and project characteristics; S4-2. Feature selection: Determine which features are most important for risk assessment;

[0061] S4-3. Model training: Train a logistic regression model using historical data;

[0062] S4-4. Model validation: Validate the effectiveness of the model through methods such as cross-validation;

[0063] S4-5. Model application: Use the trained model to conduct risk assessment on new guarantee applications.

[0064] The basic formula of logistic regression is:

[0065]

[0066] The calculation formula for probability:

[0067]

[0068] Where:

[0069] P(Y = 1|X) is the probability of the event Y = 1 (i.e., high risk) given the feature X.

[0070] β0 is the intercept term, representing the log-odds of risk when all features XX are 0.

[0071] β1, β2,..., βn are the coefficients of each feature X1, X2,..., Xn, representing the degree of influence of each feature on risk. The sign of the coefficient indicates the positive or negative correlation between the feature and risk.

[0072] X1, X2,..., Xn are feature variables, such as personal credit scores, project scales, and contract amounts.

[0073] In step S5, during the cost calculation process, the system uses a cost-based intelligent pricing algorithm, which comprehensively considers factors such as project risk, guarantee amount, and market conditions to formulate a reasonable cost standard for users.

[0074] In step S6, to ensure payment security, the system uses the RSA encryption algorithm to ensure the security of users' payment information during transmission. After successful payment, the system will immediately generate a payment voucher and provide it for users to view and download for subsequent verification and auditing.

[0075] In step S7, the insurance company or guarantee company will use the decision tree algorithm to review the application materials, which can quickly identify eligible or problematic applications. After passing the review, the insurance company or guarantee company will issue an electronic guarantee within a short time and feedback it to the user through the system.

[0076] In step S8, to ensure that users can obtain guarantee letter information in real time, the system adopts the WebSocket protocol for data synchronization to keep the user interface and server data updated in real time.

[0077] In step S9, the insurance company or guarantee company will use the support vector machine algorithm to review the extension or revocation application. This algorithm can efficiently handle complex review processes. After the review is passed, the system will handle the extension or revocation procedures for the user and update the guarantee letter status.

[0078] In step S9, to improve the quality of after-sales service, the system adopts sentiment analysis algorithms such as the SVM classification algorithm to analyze the sentiment tendency of user feedback, so as to better understand user needs, optimize service strategies, and improve user satisfaction.

[0079] In the present invention, the logistic regression algorithm can effectively identify potential high-risk guarantee letter applications by analyzing the detailed information and historical data provided by users. This process includes data preparation, feature selection, model training, verification, and application, ensuring the accuracy and reliability of risk assessment. Through this step, the system can pre-screen applications that may have default risks, thereby protecting the insurance company or guarantee company from unnecessary economic losses. This automated risk assessment mechanism greatly improves the review efficiency, reduces the workload of manual review, and also reduces the risk of misjudgment caused by human errors.

[0080] In the present invention, the classification result of the logistic regression algorithm provides clear decision-making support for users and insurance companies. For applications classified as high-risk, the system will prompt for manual review, which is not only a supplement to the insurance company's risk control but also a test of the user's integrity. This dual review mechanism ensures that only eligible applicants can obtain the guarantee letter, thus maintaining the fairness and order of the market. In addition, by quantifying the risk through the logistic regression algorithm, the insurance company can more accurately formulate the guarantee letter fee and provide a more reasonable quotation for users, which not only improves the user experience but also enhances the market competitiveness of the insurance company. Generally speaking, the application of the logistic regression algorithm has significantly improved the multi-party electronic guarantee letter mobile service method in terms of risk management and user service, providing strong support for the smooth operation of the entire service process.

[0081] It should be noted that, in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

[0082] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A mobile service method for multiple electronic letters of guarantee, characterized by: The steps include: S1: Perform user registration and authentication. After downloading and installing the Multi-element Electronic Guarantee APP on the mobile terminal, the user fills in personal information, including name, ID number, and contact information; and uploads ID card and business license certification materials for real-name authentication; S2: Select the bond product. Users browse various electronic bond products in the APP and select the appropriate bond product according to their needs, including bid bond and performance bond; S3: Fill in the application information for the letter of guarantee. The user fills in the application information according to the selected letter of guarantee product, including the project name, amount, and term; then upload the relevant contract and bidding documents and related supporting materials S4: Conduct risk assessment. The system conducts risk assessment based on the information filled in by the user and the materials uploaded; For high-risk projects, the system prompts users to conduct manual review; S5: Confirm the guarantee fee, and the user checks the guarantee fee details; after confirmation, select the payment method; S6: Make online payment. The user completes the payment of the guarantee fee through the payment function in the APP. After the payment is successful, the system generates a payment voucher; S7: Review and issue the letter of guarantee. The system will submit the application information and payment voucher. Then it will review the letter of guarantee and issue an electronic letter of guarantee after the review is passed. S8: Push and query the letter of guarantee. The system pushes the electronic letter of guarantee to the user. The user can view, download and print the electronic letter of guarantee in the APP and can query the status of the letter of guarantee and related information at any time; S9: Extend or cancel the letter of guarantee. Before the letter of guarantee expires, the user can apply for an extension in the APP. If the user needs to cancel the letter of guarantee, he can submit a cancellation application in the APP. After the application is reviewed and approved, the extension or cancellation procedures will be processed. S10: Provide after-sales service and complaint handling. If users have any questions or problems during use, they can consult the customer service function in the APP; customer service personnel will respond and solve the problems in a timely manner; users can evaluate the service, make suggestions or complaints.

2. A mobile service method for multiple electronic letters of guarantee as claimed in claim 1, characterized in that: In step S1, in order to improve the efficiency and security of authentication, the system uses an advanced face recognition algorithm to ensure the authenticity of the user's identity through face comparison; during the authentication process, the system also requires the user to perform liveness detection to prevent fraud; Once the authentication is successful, the system will create a unique personal account for the user, and the user can perform all subsequent operations through this account.

3. A mobile service method for multiple electronic letters of guarantee as claimed in claim 1, characterized in that: In step S2, the system provides a detailed product introduction, including the scope of application, advantages, and fee standards of each guarantee, so that users can choose the most suitable guarantee product based on their business needs. In addition, the system uses a content-based recommendation algorithm, which analyzes the user's personal information, historical browsing history, and industry preferences, and recommends highly matching guarantee products to users, thereby improving the user experience.

4. A mobile service method for multiple electronic letters of guarantee as claimed in claim 1, characterized in that: In step S3, in order to improve the accuracy and efficiency of information filling, the system adopts natural language processing technology, especially named entity recognition algorithm, which can automatically identify and extract key information from the user input text, reduce manual input errors, and speed up the application process.

5. The mobile service method for multiple electronic letters of guarantee according to claim 1, characterized in that: In step S4, a logistic regression algorithm is used to classify "high risk" and "low risk" in the risk assessment, specifically including: S4-1. Data preparation: Collect and preprocess data, including user characteristics and project characteristics; S4-2. Feature selection: determine which features are most important for risk assessment; S4-3. Model training: Use historical data to train a logistic regression model; S4-4. Model validation: The validity of the model was verified by cross-validation method; S4-5. Model application: Use the trained model to conduct risk assessment on new letter of guarantee applications; The basic formula for logistic regression is: The formula for calculating probability is: in: P(Y=1|X) is the probability of event Y=1 (i.e. high risk) given feature X; β0 is the intercept term, which represents the logarithmic probability of risk when all characteristics XX are 0; β1,β2,...,β n are the features X1,X2,...,X n The coefficient of indicates the degree of influence of each feature on the risk; the positive or negative value of the coefficient indicates the positive or negative correlation between the feature and the risk; X1,X2,...,X n are characteristic variables, including personal credit score, project size, and contract amount.

6. A mobile service method for multiple electronic letters of guarantee as claimed in claim 1, characterized in that: In step S5, during the fee calculation process, the system uses a cost-based intelligent pricing algorithm, which will comprehensively consider project risks, guarantee amount, and market conditions to formulate reasonable fee standards for users.

7. A mobile service method for multiple electronic letters of guarantee as claimed in claim 1, characterized in that: In step S6, in order to ensure payment security, the system uses the RSA encryption algorithm to ensure the security of the user's payment information during transmission; after the payment is successful, the system will immediately generate a payment voucher and provide it to the user for viewing and downloading, so as to facilitate subsequent verification and auditing; In step S7, the application materials are reviewed using a decision tree algorithm, which can quickly identify applications that meet the requirements or have problems. After the review is passed, an electronic letter of guarantee will be issued in a short time and fed back to the user through the system.

8. A mobile service method for multiple electronic letters of guarantee as claimed in claim 1, characterized in that: In step S8, in order to ensure that the user can obtain the guarantee letter information in real time, the system uses the WebSocket protocol to synchronize data and keep the user interface and server data updated in real time.

9. A mobile service method for multiple electronic letters of guarantee as claimed in claim 1, characterized in that: In step S9, the support vector machine algorithm is used to review the application for extension or revocation. After the review is passed, the system will handle the extension or revocation procedures for the user and update the status of the letter of guarantee.

10. A mobile service method for multiple electronic letters of guarantee as claimed in claim 1, characterized in that: In step S9, in order to improve the quality of after-sales service, the system uses a sentiment analysis algorithm to analyze the sentiment tendency of user feedback, so as to better understand user needs, optimize service strategies, and improve user satisfaction.

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